AI/ML Practice Architect

TEKsystems•Baltimore, MD

3d•$136,000 - $204,000•Remote

About The Position

Think of TEKsystems Global Services (TGS) as the growth solution for enterprises today. We unleash growth through technology, strategy, design, execution and operations with a customer-first mindset for bold business leaders. We deliver cloud, data and customer experience solutions. Our partnerships with leading cloud, design and business intelligence platforms fuel our expertise. We value deep relationships, dedication to serving others and inclusion. We drive positive outcomes for our people and our business, and we stay true to our commitments and act in harmony with our words. We exist to create significant opportunities for people to achieve fulfillment through career success. Ready to join us? Here’s what the opportunity supported through our TGS Talent Acquisition Team requires: Position Overview The AI/ML Practice Architect is a senior, hands-on technical leader who combines solution architecture and customer consultation with deep applied AI/ML engineering capability. This role accelerates practice growth by shaping repeatable offerings, guiding delivery excellence, and building production-grade AI systems — especially GenAI/LLM, retrieval, evaluation, and agentic workflows — integrated into customer platforms. This role will travel as needed, which includes customer onsite workshops, executive presentations, delivery kickoffs, and internal practice events.

Requirements

8 or more years in full stack software engineering, data engineering, ML engineering, or applied AI, including delivery in customer-facing or consulting environments
3 or more years building and deploying ML/LLM/GenAI-powered products (e.g., prompt/context engineering, RAG, evaluation, and guardrails)
Strong solution architecture skills: requirements gathering, estimation, system design, and leading technical decisions across teams
Proficiency in Python and at least one of: TypeScript/JavaScript, Java, Go, or Rust; experience building APIs and services (e.g., FastAPI, Django, Node/Express)
Experience with cloud platforms (AWS, Azure, or GCP) and containerization (Docker); familiarity with Kubernetes is a plus
Ability to influence across stakeholders (Sales, Delivery, Engineering, Product, Security/Privacy) and communicate effectively at multiple levels

Nice To Haves

Experience deploying or fine-tuning open-source LLMs (e.g., Hugging Face, vLLM, Ollama) and/or using managed services (e.g., Azure OpenAI, Bedrock, Vertex AI)
Hands-on experience with agent frameworks/orchestration (e.g., LangGraph, AutoGen, CrewAI) and tool integration patterns
Experience with vector databases and semantic search (e.g., Pinecone, Weaviate, Chroma) and advanced RAG/knowledge graph techniques
Experience leading small teams or technical workstreams while remaining hands-on
Exposure to regulated environments (e.g., Finance & Healthcare) and formal security/privacy review processes

Responsibilities

Drive measurable customer outcomes by designing and delivering AI/ML and GenAI solutions end-to-end (discovery → build → deploy → operate).
Scale TGS AI capabilities through reusable playbooks, reference architectures, accelerators, and enablement.
Partner with Sales and Delivery to shape, price, and win work; serve as a trusted advisor in pre- and post-sales engagements.
Lead customer discovery and value definition: map current/future-state workflows, define success metrics, and translate business goals into technical requirements.
Design solution architectures and delivery approaches; document assumptions, risks, dependencies, and cost/effort estimates.
Create and maintain practice solution content: reference architectures, accelerators, templates, delivery playbooks, and pricing guidance.
Partner with Sales, Solutioning, Delivery Leadership, and Practice Directors on pre-sales strategy, proposals, and executive communications.
Mentor consultants/engineers: design reviews, technical coaching, and best-practice enablement across engagements.
Continuously optimize delivery processes, promote reuse, and champion innovation rooted in measurable customer impact.
Build and ship production AI/ML systems including model integration, data pipelines, services, and user experiences.
Design and implement GenAI/LLM solutions: prompt & context engineering, RAG grounding, embedding/vector store strategies, and latency/quality trade-offs.
Evolve prompted workflows into agentic AI: durable execution, tool use, memory, orchestration (single- and multi-agent), and human-in-the-loop gates.
Establish evaluation and experimentation rigor: offline/online tests, human + AI evaluation rubrics, error taxonomies, and KPI instrumentation.
Implement security, safety, and compliance controls: PII handling, prompt-injection mitigations, model risk management, and red-teaming.
Operationalize MLOps/LLMOps: CI/CD, model/version management, observability, drift/feedback loops, and incident response.
Drive execution cadence with clear gates, owners, and KPIs from discovery through launch.
Hold self and others accountable to commitments; proactively resolve issues before they impact customers.
Communicate trade-offs, impact, and risk to technical and non-technical stakeholders; produce executive-ready narratives and artifacts.
Support practice hiring/interviewing and capability building as needed; contribute to internal communities of practice.